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Record W3087759305 · doi:10.1093/jas/skaa299

Optimum roughage proportion in barley-based feedlot cattle diets: growth performance, feeding behavior, and carcass traits

2020· article· en· W3087759305 on OpenAlexafffundabout
K. M. Koenig, Gwinyai E Chibisa, G.B. Penner, K. A. Beauchemin

Bibliographic record

VenueJournal of Animal Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of SaskatchewanAgriculture and Agri-Food Canada
FundersAlberta Livestock and Meat Agency
KeywordsFeedlotDry matterAnimal scienceSilageBiologyBeef cattleFeed conversion ratioCrossbreedWeight gainBody weight

Abstract

fetched live from OpenAlex

High grain diets are fed to finishing beef cattle to maximize animal performance in a cost-effective manner. However, a small amount of roughage is incorporated in finishing diets to help prevent ruminal acidosis, although few studies have examined optimum roughage inclusion level in barley-based diets. The objective of the study was to evaluate the effects of roughage proportion in barley-based finishing diets on growth performance, feeding behavior, and carcass traits of feedlot cattle. Crossbred beef steers (n = 160; mean body weight ± SD, 349.7 ± 21.4 kg) were allocated to 20 pens that were assigned randomly to four dietary treatments (five pens of eight steers per treatment). The treatment diets contained barley silage at 0%, 4%, 8%, and 12% of dietary dry matter (DM). The remainder of the diets (DM basis) consisted of 80%, 76%, 72%, and 68% barley grain, respectively, 15% corn dried distiller's grains, 5% mineral and vitamin supplement, and 32 mg monensin/kg diet DM. The diets were fed as total mixed rations for ad libitum intake (minimum of 5% refusal) once per day. Cattle were weighed on 2 consecutive days at the start and end of the experiment and on 1 d every 3 wk throughout the experiment (124 d). Two pens for each treatment group were equipped with an electronic feeding system (GrowSafe Systems Ltd., Calgary, Alberta) to monitor feed intake and feeding behavior of individual cattle. The data for dry matter intake (DMI), average daily gain (ADG), gain:feed (G:F) ratio, and carcass traits were analyzed as a completely randomized design with fixed effect of barley silage proportion and pen replicate as experimental unit. Feeding behavior data were analyzed similarly, but with animal as experimental unit. Averaged over the study, DMI increased linearly (11.1, 11.3, 11.7, 11.8 kg/d; P = 0.001) as barley silage proportion increased from 0%, 4%, 8%, and 12% of DM, but ADG was not affected (carcass-adjusted,1.90, 1.85, 1.87, 1.89 kg/d; P ≥ 0.30). Consequently, G:F ratio decreased linearly (carcass-adjusted, 168.9, 163.8, 158.5, 160.6 g/kg DMI; P = 0.023). When averaged over the study, proportion of barley silage in the diet had no linear or quadratic effects (P > 0.10) on meal frequency, duration of meals, intermeal duration, or meal size, but eating rate decreased linearly with increasing silage proportion (P = 0.008). There was no diet effect on liver abscesses (P ≥ 0.92), and effects on carcass characteristics were minor or nonexistent. We conclude that increasing the proportion of barley silage in a feedlot finishing diet at the expense of barley grain to minimize the incidence of ruminal acidosis may decrease feed conversion efficiency.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.248
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2020
Admission routes3
Has abstractyes

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